Motivation on Early Detection of Cervical Cancer in Women of Reproductive Age: A Scoping Review
Bibliographic record
Abstract
ABSTRACT Background: Cervical cancer ranked the fourth most cancer incidence in women. WHO announced that 311,000 women died from the disease in 2018. Cervical cancer screening uptake remains low, especially in low- and middle-income countries. This scoping review aimed to investigate the motivation for early detection of cervical cancer in women of reproductive age. Subjects and Method: A scoping review method was conducted in eight stages including (1) Identification of study problems; (2) Determining priority problem and study question; (3) Determining framework; (4) Literature searching; (5) Article selection; (6) Critical appraisal; (7) Data extraction; and (8) Mapping. The research question was identified using population, exposure, and outcome(s) (PEOS) framework. The search included PubMed, ResearchGate, and grey literature through the Google Scholar search engine databases. The inclusion criteria were English-language and full-text articles published between 2010 and 2020. A total of 275 articles were obtained by the searched database. After the review process, twelve articles were eligible for this review. The quality of searched articles was appraised by Joanna Briggs Institute Critical Appraisal tools. The data were reported by the PRISMA flow chart. Results: Seven articles from developing countries (Jamaica, Nepal, Africa, Nigeria, Libya, and Uganda) and five articles from developed countries (England, Canada, Sweden, and Japan) met the inclusion criteria with cross-sectional studies. The selected existing studies discussed 3 main themes related to motivation to early detection of cervical cancer, namely sexual and reproductive health problems, diseases, and influence factors. Conclusion: Motivation for cervical cancer screening uptake is strongly related to the early detection of cervical cancer among reproductive-aged women. Client-centered counseling and comprehensive sexual and reproductive health education play an important role in delivering information about the importance of cervical cancer screening. Keywords: motivation, cervical cancer, screening, early detection, reproductive-aged Correspondence: Siti Nurul Khotimah. Health Sciences Department of Master Program, Universitas Aisyiyah Yogyakarta. Jl. Siliwangi (Ringroad Barat) No. 63, Nogotirto, Gamping, Sleman, Yogyakarta, 55292. Email: Sitinurulkhotimah1988@gmail.com. Mobile: +6281227888442. DOI: https://doi.org/10.26911/the7thicph.03.65
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".